Network Science: SI Dynamics of Disease Spread — Presentation Detail — Celebration of Scholars

Instructions

Student presentations must have a faculty sponsor.

Abstracts must include a title and a description of the research, scholarship, or creative work. The description should be 150-225 words in length and constructed in a format or style appropriate for the presenter’s discipline.

The following points should be addressed within the selected format or style for the abstract:

A clear statement of the problem or question you pursued, or the scholarly goal or creative theme achieved in your work.
A brief comment about the significance or uniqueness of the work.
A clear description of the methods used to achieve the purpose or goals for the work.
A statement of the conclusions, results, outcomes, or recommendations, or if the work is still in progress, the results you expect to report at the event.

Presenter photographs should be head and shoulder shots comparable to passport photos.

Additional Information

More information is available at carthage.edu/celebration-scholars/. The following are members of the Research, Scholarship, and Creativity Committee who are eager to listen to ideas and answer questions:

Jun Wang
Kim Instenes
John Kirk
Nora Nickels
Andrew Pustina
James Ripley

Network Science: SI Dynamics of Disease Spread

Name: Elisabeth Rutter
Major: Chemistry/Mathematics
Hometown: Lena, IL
Faculty Sponsor: Haley Yaple
Other Sponsors:
Type of research: SURE
Funding: SURE

Name: Catherine Northrup
Major: Mathematics
Hometown: Rochestet, MN
Faculty Sponsor: Haley Yaple
Other Sponsors:
Type of research: SURE
Funding: SURE

Name: Kerry Stapf
Major: Mathematics
Hometown: Prior Lake, MN
Faculty Sponsor: Haley Yaple
Other Sponsors:
Type of research: SURE
Funding: SURE

Abstract

Imagine you are walking down a crowded hallway. You aren’t in contact with everyone all at once. You talk to or simply pass by different people at different times as you walk down the hall. These connections would best be represented using a temporal network. In this work, we examine temporal networks to determine the behavior of disease spread across these networks and how it differs from the behavior of static networks. We use differential equations for mean field approximations to theoretically model how infection spreads throughout a temporal network. We extend our model to incorporate network structure by deriving a degree-based mean field theory. We then validate our theories with simulations in Mathematica. We also look into including multiple rounds of infections to see how it affects the spreading behavior. From our results we are able to determine how the temporal aspect affects the rate of spread of the disease and the overall size of the infected population.

Poster file